House price inflation modelling
The sold date is a feature like any other. You can do this as follow. I am assuming the features are in a pandas data frame  called df where the column date is called date. Easiest way is to use the pandas to_datetime function. Documentation is here.
def encode_dates(df, column):
    df = df.copy()
    df[column] = pd.to_datetime(df[column] )
    df[column + '_year'] = df[column].apply(lambda x: x.year)
    df[column + '_month'] = df[column].apply(lambda x: x.month)
    df[column + '_day'] = df[column].apply(lambda x: x.day)
    df = df.drop(column, axis=1)
    return df
df=encode_dates(df. 'date')

This function will modify the df data frame. It will create 3 new columns labeled
date year, date month and date day and it will remove the date column from the data frame.
Now these new columns along with the other features can be used to train your model.